Background: The prevalence of diabetes is on rise worldwide and environmental factors are being increasingly recognized to be involved in this rise. An emerging body of evidence has evaluated the impact of long-term exposure to noise on diabetes mellitus, highlighting the need to synthesize this evidence.
Objectives: To systematically review and conduct meta-analysis of the available evidence on the association between long-term exposure to transport and occupational noise exposure and diabetes mellitus.
Methods: Selected databases were searched for available evidence published till September 13th, 2017 following MOOSE guidelines. The quality of articles was assessed using the Newcastle-Ottawa Scale. Random effects meta-analysis was applied to abstract combined estimates for diabetes mellitus per 5 dB increase in noise exposure. We evaluated the heterogeneity applying Cochran's Q test and quantified it using I statistic. Meta-regressions were conducted to identify sources of heterogeneity. Publication bias was evaluated using funnel plot and Egger's test.
Results: Fifteen studies met our inclusion criteria of which nine including five prospective cohorts, two cross-sectional and two case-control studies with a total number of 444460 adult participants and 17430 diabetes mellitus cases included in meta-analyses. We observed a 6% (95% confidence interval (CI): 3%, 9%) increase in the risk of diabetes mellitus per 5 dB increase in noise exposure regardless of its source. Source-specific analyses were suggestive for stronger associations for air traffic noise (combined odds ratio: 1.17; 95% CI: 1.06, 1.29 per 5 dB increase in exposure) flowed by road traffic noise (combined odds ratio: 1.07; 95% CI: 1.02, 1.12). We observed some indications of publication bias; however the findings were robust after trim and fill test. Meta-regression analyses showed that the adjustment in general, and not specifically related to air pollution, could predict the between-study heterogeneity in reported associations.
Conclusions: The results indicate an increased risk of diabetes mellitus associated with noise exposure, mainly related to air and road traffic.
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http://dx.doi.org/10.1016/j.envres.2018.05.011 | DOI Listing |
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFToxicol Rep
December 2024
Department of Occupational Health and Ergonomic, Qazvin Medical University, Qazvin, Iran.
Occupational exposures are generally complex, workers are exposed with more than one hazardous agent in work environment. Combined exposure to noise and benzene is common in occupational environments. Sub-acute exposure to benzene vapors can induce oxidative stress in serum.
View Article and Find Full Text PDFEnviron Pollut
December 2024
Department of Epidemiology and Health Statistics, School of Public Health, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China; Key Laboratory of Environmental Factors and Chronic Disease Control, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China. Electronic address:
Current literature lacks information regarding impacts of green spaces on susceptibility to cardiovascular disease (CVD) related to harmful environmental exposures. The UK Biobank cohort study was utilized to investigate whether green spaces can mitigate risks associated with air pollutants, nighttime light, noise, and traffic intensity. Latent Profile Analysis was performed on green spaces and adverse environmental exposures in order to assess individual level exposure.
View Article and Find Full Text PDFEnviron Int
December 2024
MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK; National Institute for Health Research Health Protection Research Unit in Chemical and Radiation Threats and Hazards, School of Public Health, Imperial College London, UK. Electronic address:
Background: Although there is increasing evidence that environmental exposures are associated with the risk of neurodegenerative conditions, there is still limited mechanistic evidence evaluating potential mediators in human populations.
Methods: UK Biobank is a large long-term study of 500,000 adults enrolled from 2006 to 2010 age 40-69 years. ICD-10 classified reports of dementia cases up to 2022 (Alzheimer's disease, vascular dementia, dementia in other classified diseases, and unspecified dementia) were identified from health record linkage.
J Urban Health
December 2024
Department of Environmental Sciences, Faculty of Natural Sciences, Vytautas Magnus University, Kaunas, Lithuania.
Environmental noise pollution is one of the biggest concerns and the most important challenges in urban areas. Evidence from epidemiological studies shows that acoustic pollution can impact human health, and the effects may be stronger in susceptible and sensitive individuals. The objective of the study was to determine the individual exposure to road transport noise for preschool children in the residential environment and to assess its impact on children's psychological health.
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